AI-Enhanced Decision-Making for Sustainable Supply Chains: Reducing Carbon Footprints in the USA

TL;DR

This paper explores how AI technologies like machine learning and optimization can help US companies make more sustainable supply chain decisions, reducing carbon footprints and meeting regulatory and consumer sustainability demands.

Contribution

It provides a comprehensive review of AI-driven solutions for sustainable supply chains in the US, highlighting challenges and opportunities for implementation.

Findings

AI can significantly reduce supply chain emissions.

Machine learning improves predictive accuracy for sustainability metrics.

Optimization algorithms enhance resource efficiency in supply chains.

Abstract

Organizations increasingly need to reassess their supply chain strategies in the rapidly modernizing world towards sustainability. This is particularly true in the United States, where supply chains are very extensive and consume a large number of resources. This research paper discusses how AI can support decision-making for sustainable supply chains with a special focus on carbon footprints. These AI technologies, including machine learning, predictive analytics, and optimization algorithms, will enable companies to be more efficient, reduce emissions, and display regulatory and consumer demands for sustainability, among other aspects. The paper reviews challenges and opportunities regarding implementing AI-driven solutions to promote sustainable supply chain practices in the USA.